Eco-friendly multi-skilled worker assignment and assembly line balancing problem

Abstract Workforce assignment and energy consumption impact greatly on the manufacturing performance. In this work, we study a multi-skilled worker assignment and assembly line balancing problem with the consideration of energy consumption. The problem consists of scheduling products and assigning workers to workstations appropriately under a given cycle time. Two objectives are minimized simultaneously, i.e., (1) the total costs including the processing cost and the fixed cost induced by employing workers, and (2) the energy consumption. A bi-objective mixed-integer linear programming model is formulated and an ϵ -constraint method is adopted to obtain the Pareto front for small-scale problems. For solving large-size problems, a processing time and energy consumption sorted-first rule (PT-EC SFR), a multi-objective genetic algorithm (NSGA-II) and a multi-objective simulated annealing method (MOSA) are developed. Numerical experiments are conducted and computational results show that the designed PT-EC SFR outperforms the other two algorithms in terms of computational time and quality of solutions.

[1]  Mohamed Mazouzi,et al.  An integrated model for assembly line re-balancing problem , 2018, Int. J. Prod. Res..

[2]  Alysson M. Costa,et al.  The assembly line worker assignment and balancing problem with stochastic worker availability , 2016 .

[3]  Mayron César O. Moreira The assembly line worker integration and balancing problem , 2015, Comput. Oper. Res..

[4]  Steve Evans,et al.  Understanding the hidden cost and identifying the root causes of changeover impacts , 2017 .

[5]  L. Lasdon,et al.  On a bicriterion formation of the problems of integrated system identification and system optimization , 1971 .

[6]  Bin Guo,et al.  VTracer: When Online Vehicle Trajectory Compression Meets Mobile Edge Computing , 2020, IEEE Systems Journal.

[7]  Ming Liu,et al.  An exact method for disassembly line balancing problem with limited distributional information , 2020, Int. J. Prod. Res..

[8]  Olivier Grunder,et al.  Productivity improvement through a sequencing generalised assignment in an assembly line system , 2017 .

[9]  Sanjoy Kumar Paul,et al.  Sustainable operator assignment in an assembly line using genetic algorithm , 2012 .

[10]  X. Xing,et al.  Optimal design of distributed energy systems for industrial parks under gas shortage based on augmented ε-constraint method , 2019, Journal of Cleaner Production.

[11]  Mohammad Hossein Fazel Zarandi,et al.  An enhanced NSGA-II algorithm for fuzzy bi-objective assembly line balancing problems , 2018, Comput. Ind. Eng..

[12]  Kadir Buyukozkan,et al.  U-shaped assembly line worker assignment and balancing problem: A mathematical model and two meta-heuristics , 2017, Comput. Ind. Eng..

[13]  George Q. Huang,et al.  A NSGA-II based memetic algorithm for multiobjective parallel flowshop scheduling problem , 2017, Comput. Ind. Eng..

[14]  Christian Blum,et al.  On solving the assembly line worker assignment and balancing problem via beam search , 2011, Comput. Oper. Res..

[15]  J. Pereira,et al.  An exact approach for the robust assembly line balancing problem , 2017 .

[16]  Reza Ramezanian,et al.  Modeling and solving multi-objective mixed-model assembly line balancing and worker assignment problem , 2015, Comput. Ind. Eng..

[17]  Wei Liu,et al.  A new double flexible job-shop scheduling problem integrating processing time, green production, and human factor indicators , 2018 .

[18]  G. J. C. Gaalman,et al.  On the who-rule in Dual Resource Constrained (DRC) manufacturing systems , 2004 .

[19]  Mostafa Zandieh,et al.  Assembly line balancing by a new multi-objective differential evolution algorithm based on TOPSIS , 2011 .

[20]  S. Bandyopadhyay,et al.  Solving multi-objective parallel machine scheduling problem by a modified NSGA-II , 2013 .

[21]  Dvir Shabtay,et al.  Single machine scheduling with two competing agents and equal job processing times , 2015, Eur. J. Oper. Res..

[22]  Tolga Bektas,et al.  Combinatorial Benders cuts for assembly line balancing problems with setups , 2017, Eur. J. Oper. Res..

[23]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[24]  Zhiwen Yu,et al.  ROD-Revenue: Seeking Strategies Analysis and Revenue Prediction in Ride-on-Demand Service Using Multi-Source Urban Data , 2020, IEEE Transactions on Mobile Computing.

[25]  Alexandre Dolgui,et al.  Workforce Minimization for a Mixed-Model Assembly Line , 2014 .

[26]  S. Afshin Mansouri,et al.  Green scheduling of a two-machine flowshop: Trade-off between makespan and energy consumption , 2016, Eur. J. Oper. Res..

[27]  Qiuzhen Lin,et al.  A novel hybrid multi-objective immune algorithm with adaptive differential evolution , 2015, Comput. Oper. Res..

[28]  Alexandre Dolgui,et al.  Optimal workforce assignment to operations of a paced assembly line , 2018, Eur. J. Oper. Res..

[29]  George Mavrotas,et al.  Effective implementation of the epsilon-constraint method in Multi-Objective Mathematical Programming problems , 2009, Appl. Math. Comput..

[30]  Ming Liu,et al.  Service-oriented bi-objective robust collection-disassembly problem with equipment selection , 2020, Int. J. Prod. Res..

[31]  Ming Liu,et al.  Stochastic Runway Scheduling Problem With Partial Distribution Information of Random Parameters , 2020, IEEE Access.

[32]  David A. Nembhard,et al.  Selection policies for a multifunctional workforce , 2014 .

[33]  Fariborz Jolai,et al.  A Simulated Annealing algorithm for a mixed model assembly U-line balancing type-I problem considering human efficiency and Just-In-Time approach , 2013, Comput. Ind. Eng..

[34]  Lei Wang,et al.  Integrated green scheduling optimization of flexible job shop and crane transportation considering comprehensive energy consumption , 2019, Journal of Cleaner Production.

[35]  Ming Liu,et al.  Robust disassembly line balancing with ambiguous task processing times , 2019, Int. J. Prod. Res..

[36]  Feifeng Zheng,et al.  A two-stage stochastic programming for single yard crane scheduling with uncertain release times of retrieval tasks , 2019 .

[37]  Meral Azizoglu,et al.  Flexible assembly line design problem with fixed number of workstations , 2011 .

[38]  Jonathan A. Wright,et al.  Constrained, mixed-integer and multi-objective optimisation of building designs by NSGA-II with fitness approximation , 2015, Appl. Soft Comput..

[39]  Ming Liu,et al.  Energy-oriented bi-objective optimization for the tempered glass scheduling , 2020 .

[40]  Liping Zhang,et al.  Mathematical model and grey wolf optimization for low-carbon and low-noise U-shaped robotic assembly line balancing problem , 2019, Journal of Cleaner Production.

[41]  Weidong Li,et al.  A Systematic Approach of Process Planning and Scheduling Optimization for Sustainable Machining , 2015, Sustainable Manufacturing and Remanufacturing Management.

[42]  Raymond Chiong,et al.  Solving the energy-efficient job shop scheduling problem: a multi-objective genetic algorithm with enhanced local search for minimizing the total weighted tardiness and total energy consumption , 2016 .

[43]  Daqing Zhang,et al.  crowddeliver: Planning City-Wide Package Delivery Paths Leveraging the Crowd of Taxis , 2017, IEEE Transactions on Intelligent Transportation Systems.

[44]  Marcus Ritt,et al.  A memetic algorithm for the cost-oriented robotic assembly line balancing problem , 2018, Comput. Oper. Res..

[45]  Zhu Wang,et al.  TrajCompressor: An Online Map-matching-based Trajectory Compression Framework Leveraging Vehicle Heading Direction and Change , 2020, IEEE Transactions on Intelligent Transportation Systems.

[46]  Feng Chu,et al.  A multi-objective distribution-free model and method for stochastic disassembly line balancing problem , 2019, Int. J. Prod. Res..

[47]  Alkin Yurtkuran,et al.  A novel artificial bee colony algorithm for the workforce scheduling and balancing problem in sub-assembly lines with limited buffers , 2018, Appl. Soft Comput..

[48]  Ada Che,et al.  Energy-efficient no-wait permutation flow shop scheduling by adaptive multi-objective variable neighborhood search , 2020 .

[49]  Alexandre Dolgui,et al.  Minimizing the number of workers in a paced mixed-model assembly line , 2019, Eur. J. Oper. Res..

[50]  Marco Laumanns,et al.  Performance assessment of multiobjective optimizers: an analysis and review , 2003, IEEE Trans. Evol. Comput..

[51]  Ujjwal Maulik,et al.  A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA , 2008, IEEE Transactions on Evolutionary Computation.

[52]  Fantahun M. Defersha,et al.  Simultaneous balancing, sequencing, and workstation planning for a mixed model manual assembly line using hybrid genetic algorithm , 2018, Comput. Ind. Eng..

[53]  MengChu Zhou,et al.  An Improved Exact $\varepsilon$-Constraint and Cut-and-Solve Combined Method for Biobjective Robust Lane Reservation , 2015, IEEE Transactions on Intelligent Transportation Systems.

[54]  Jordi Pereira,et al.  A branch-and-bound algorithm for assembly line worker assignment and balancing problems , 2014, Comput. Oper. Res..

[55]  Alexandre Dolgui,et al.  The stability radius of an optimal line balance with maximum efficiency for a simple assembly line , 2019, Eur. J. Oper. Res..

[56]  Carlos A. Coello Coello,et al.  Solving Multiobjective Optimization Problems Using an Artificial Immune System , 2005, Genetic Programming and Evolvable Machines.

[57]  Bopaya Bidanda,et al.  Worker assignment in cellular manufacturing considering technical and human skills , 2002 .

[58]  Yong Yin,et al.  A multi-skilled worker assignment problem in seru production systems considering the worker heterogeneity , 2018, Comput. Ind. Eng..

[59]  Sebnem Yilmaz Balaman Investment planning and strategic management of sustainable systems for clean power generation: An ε-constraint based multi objective modelling approach , 2016 .